Retrieve version of Python engine & packages
This commit is contained in:
Adi Eldar
Родитель
08587a0d45
Коммит
42a584e9e1
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//
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// get_modules_version_sf()
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//
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// Returns version information for the Python engine and the specified packages
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//
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.create function with (folder = "Packages\\Utils", docstring = "Returns version information for the Python engine and the specified packages")
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get_modules_version_sf(modules:(*))
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{
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let code =
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'import importlib\n'
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'import sys\n'
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'\n'
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'result = df\n'
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'for i in range(df.shape[0]):\n'
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' try:\n'
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' m = importlib.import_module(df.iloc[i, 0])\n'
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' result.loc[i, "ver"] = m.__version__\n'
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' except Exception as ex:\n'
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' result.loc[i, "ver"] = ex.msg\n'
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'id = df.shape[0]\n'
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'result.loc[id, df.columns[0]] = "Python"\n'
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'result.loc[id, "ver"] = sys.version\n';
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modules | evaluate python(code, 'df(*),ver:string')
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}
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let get_modules_version_sf = (modules:(*))
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{
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let code =
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'import importlib\n'
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'import sys\n'
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'\n'
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'result = df\n'
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'for i in range(df.shape[0]):\n'
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' try:\n'
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' m = importlib.import_module(df.iloc[i, 0])\n'
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' result.loc[i, "ver"] = m.__version__\n'
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' except Exception as ex:\n'
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' result.loc[i, "ver"] = ex.msg\n'
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'id = df.shape[0]\n'
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'result.loc[id, df.columns[0]] = "Python"\n'
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'result.loc[id, "ver"] = sys.version\n';
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modules | evaluate python(code, 'df(*),ver:string')
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}
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;
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datatable(module:string)
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['numpy', 'scipy', 'pandas', 'matplotlib', 'statsmodels', 'sklearn', 'azure.kusto.data', 'adal', 'tensorflow', 'keras']
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| invoke get_modules_version_sf()
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41
index.md
41
index.md
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# azure-kusto-analytics-lib Repository
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1. **Time Series Analysis**
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1. **KqlMagic**
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* Functions
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1. [Getting Started with KqlMagic](./KqlMagic/Getting-Started-With-KqlMagic-on-ADX.ipynb)
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1. [blackman_filter_sf()](./Series/functions/blackman_filter.csl) - Create a Blackman window low pass filter of specific width
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1. [series_fit_poly_sf()](./Series/functions/series_fit_poly.csl)<sup>[1](#footnotes)</sup> - Fit a polynomial of a specified degree to a series
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1. [series_fit_sine_sf()](./Series/functions/series_fit_sine.csl)<sup>[1](#footnotes)</sup> - Fit a sine wave to a series
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1. [series_moving_avg_sf()](./Series/functions/series_moving_avg.csl) - Moving average of specific width
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1. [series_partial_sf()](./Series/functions/series_partial.csl) - Test for series with empty bins
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1. [series_rolling_sf()](./Series/functions/series_rolling.csl)<sup>[1](#footnotes)</sup> - Rolling window functions on a series
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1. [series_segment_sf()](./Series/functions/series_segment.csl) - Sequental numbering of non zero segments of a boolean series
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1. [series_summarize_sf()](./Series/functions/series_summarize.csl)<sup>[1](#footnotes)</sup> - Aggregation functions on a series
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* Queries
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1. **Labs**
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1. [Time Series Analysis Tutorial](./Series/queries/Time-Series-Analysis-Tutorial.csl) - Walkthrough of typical series functions from each category
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1. [Custom Time Series Forecasting](./Lab/Custom-Time-Series-Forcasting/Time-Series-Forcast-Walkthrough.csl)<sup>[1](#footnotes)</sup> - build and tailor a time series forecasting model from lower level functions. See task [readme](./Lab/Custom-Time-Series-Forcasting/Time-Series-Forcast-Readme.docx) file
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1. [Classification](./Lab/Classifier) - [Build a classifier in Python](./Lab/Classifier/Prediction-of-Room-Occupancy-from-Kusto-Table-with-Kqlmagic.ipynb), [score](./Lab/Classifier/Classifier-Scoring.csl)<sup>[1](#footnotes)</sup> in ADX. See task [readme](./Lab/Classifier/Classifier-Readme.docx) file
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1. **Machine Learning**
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@ -32,14 +25,28 @@
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1. [Training a classifier](./ML/notebooks/Prediction-of-Room-Occupancy-from-Kusto-Table-with-Kqlmagic.ipynb) - Demo of building and training a classifier for prediction of room occupancy in Jupyter using KqlMagic. Scoring is done using the Python plugin (see the [previous tutorial](./ML/queries/Python-Plugin-Tutorial.csl))
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1. **KqlMagic**
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1. **Time Series Analysis**
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1. [Getting Started with KqlMagic](./KqlMagic/Getting-Started-With-KqlMagic-on-ADX.ipynb)
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* Functions
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1. [blackman_filter_sf()](./Series/functions/blackman_filter.csl) - Create a Blackman window low pass filter of specific width
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1. [series_fit_poly_sf()](./Series/functions/series_fit_poly.csl)<sup>[1](#footnotes)</sup> - Fit a polynomial of a specified degree to a series
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1. [series_fit_sine_sf()](./Series/functions/series_fit_sine.csl)<sup>[1](#footnotes)</sup> - Fit a sine wave to a series
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1. [series_moving_avg_sf()](./Series/functions/series_moving_avg.csl) - Moving average of specific width
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1. [series_partial_sf()](./Series/functions/series_partial.csl) - Test for series with empty bins
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1. [series_rolling_sf()](./Series/functions/series_rolling.csl)<sup>[1](#footnotes)</sup> - Rolling window functions on a series
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1. [series_segment_sf()](./Series/functions/series_segment.csl) - Sequental numbering of non zero segments of a boolean series
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1. [series_summarize_sf()](./Series/functions/series_summarize.csl)<sup>[1](#footnotes)</sup> - Aggregation functions on a series
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* Queries
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1. [Time Series Analysis Tutorial](./Series/queries/Time-Series-Analysis-Tutorial.csl) - Walkthrough of typical series functions from each category
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1. **Utils**
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* Functions
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1. [get_modules_version_sf()](./Utils/functions/get_modules_version.csl)<sup>[1](#footnotes)</sup> - Aggregation functions on a series
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1. **Labs**
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1. [Custom Time Series Forecasting](./Lab/Custom-Time-Series-Forcasting/Time-Series-Forcast-Walkthrough.csl)<sup>[1](#footnotes)</sup> - build and tailor a time series forecasting model from lower level functions. See task [readme](./Lab/Custom-Time-Series-Forcasting/Time-Series-Forcast-Readme.docx) file
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1. [Classification](./Lab/Classifier) - [Build a classifier in Python](./Lab/Classifier/Prediction-of-Room-Occupancy-from-Kusto-Table-with-Kqlmagic.ipynb), [score](./Lab/Classifier/Classifier-Scoring.csl)<sup>[1](#footnotes)</sup> in ADX. See task [readme](./Lab/Classifier/Classifier-Readme.docx) file
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<f name="footnotes">
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